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UNIRAZAK Library Pustaka
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| Abstract : UNIRAZAK Library |
| In the digital age, businesses are under increasing pressure to improve the quality of customer service by implementing data-driven strategies. The research, titled "Leveraging Data Analytics to Improve Customer Service Quality in the Digital Age," investigates the effective utilization of data analytics to improve service performance in industries that serve customers. The investigation investigates the influence of four critical components—Data Collection, Data Analysis, Data Application, and Customer Insights—on the quality of customer service. The quantitative methodology employed in the research involves the distribution of surveys to 187 customer service managers and business analysts from business process outsourcing (BPO) companies in the Klang Valley. To evaluate the impact of the independent variables on the dependent variable, customer service quality, multiple regression techniques were used. The results from Chapter 4 confirm that all four independent variables—data collection (β = 0.157, p = 0.020), data analysis (β = 0.327, p < 0.001), data application (β = 0.171, p = 0.007), and customer insights (β = 0.343, p < 0.001)— significantly and positively impact customer service quality. Customer insights had the strongest influence, followed by data analysis, data application, and data collection. The R-squared value of 0.775 indicates that 77.5% of the variance in customer service quality is explained by these four variables. The results confirm that all four components have a substantial influence on service quality, with Customer Insights demonstrating the most significant positive effect. Businesses can collect and interpret consumer information through data collection and analysis, which results in more efficient and personalized service delivery. Data Application guarantees that insights obtained from analytics are transformed into actionable enhancements, while customer Insights enable organizations to comprehend and anticipate what customers want. Limitations, including the geographical focus, sample size, and potential common method bias, were recognized, despite the strong empirical support for the study's hypotheses. Future research should consider the following: conducting longitudinal studies to monitor the long-term impact of data analytics on customer service, expanding the scope to other regions and industries, and utilizing multiple data acquisition methods. This study offers organizations that are interested in improving customer service through data analytics actionable insights. It emphasizes the necessity of implementing a comprehensive, data-driven strategy to satisfy the changing expectations of customers in the digital age. |
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